As Large Language Models (LLMs) become more advanced, the security risks they pose also increase. Ensuring that LLM behavior aligns with human values, particularly in mitigating jailbreak attacks with elusive and implicit intentions, has become a significant challenge. To address this issue, we propose a jailbreak defense method called Real Intentions Defense (RID), which involves two phases: soft extraction and hard deletion. In the soft extraction phase, LLMs are leveraged to extract unbiased, genuine intentions, while in the hard deletion phase, a greedy gradient-based algorithm is used to remove the least important parts of a sentence, based on the insight that words with smaller gradients have less impact on its meaning. We conduct extensive experiments on Vicuna and Llama2 models using eight state-of-the-art jailbreak attacks and six benchmark datasets. Our results show a significant reduction in both Attack Success Rate (ASR) and Harmful Score of jailbreak attacks, while maintaining overall model performance. Further analysis sheds light on the underlying mechanisms of our approach.

When consumers’ shopping needs are concentrated, they are more interested in the collection of commodities under the specific marketing theme. Therefore, mining marketing themes and their commodities collections can help customers save shopping costs and improve user clicks and purchases for recommendation system. However, the current system invites experts to write marketing themes and select the relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. Therefore, we propose a automatic marketing theme and commodity construction system, which can not only generate popular marketing themes and select the relevant commodities automatically, but also improve the theme online effectiveness in the recommendation system. Specifically, we firstly utilize the pretrained language model to generate the marketing themes. And then, we utilize the theme-commodity consistency module to select the relevant commodities for the above generative theme. What’s more, we also build the indicator simulator to evaluate the effectiveness of the above generative theme. When the indicator is lower, the above selective commodities will be input into the theme-rewriter module to generate more efficient marketing themes. Finally, we utilize the human screening to control the system quality. Both the offline experiments and online A/B test demonstrate the superior performance of our proposed system compared with state-of-the-art methods.

Deep semantic retrieval has achieved remarkable success in online E-commerce applications. The majority of methods aim to distinguish positive items and negative items for each query by utilizing margin loss or softmax loss. Despite their decent performance, these methods are highly sensitive to hyper-parameters, i.e., margin and temperature 𝜏, which measure the similarity of negative pairs and affect the distribution of items in metric space. How to design and choose adaptively parameters for different pairs is still an open challenge. Recently several methods have attempted to alleviate the above problem by learning each parameter through trainable/statistical methods in the recommendation. We argue that those are not suitable for retrieval scenarios, due to the agnosticism and diversity of the queries. To fully overcome this limitation, we propose a novel adaptive metric learning method that designs a simple and universal hyper-parameter-free learning method to improve the performance of retrieval. Specifically, we first propose a method that adaptive obtains the hyper-parameters by relying on the batch similarity without fixed or extra-trainable hyper-parameters. Subsequently, we adopt a symmetric metric learning method to mitigate model collapse issues. Furthermore, the proposed method is general and sheds a highlight on other fields. Extensive experiments demonstrate our method significantly outperforms previous methods on a real-world dataset, highlighting the superiority and effectiveness of our method. This method has been successfully deployed on an online E-commerce search platform and brought substantial economic benefits.